research-code-skill

# Research Code Skill > **For developers (human readers):** all documentation in this skill is written > in English, which is the source of truth. The agent always loads the **English** > originals. A skill that acts as the **coding-standard layer** of a research lab's ML/DL project. Rather than reviewing code after the fact, it lives in the agent's context and **constrains every code operation as it happens**. Its essence is twofold: 1. **Project architecture** 鈥?keep the lab's fixed project structure intact. As the agent writes or edits code, the architecture is the stable base: do not break it, do not invent a new layout, keep it clear and consistent. 2. **Code-level conventions** 鈥?within that architecture, every symbol and call follows uniform rules: naming, formatting, import/call style, and how configs compose. These two axes are enforced in both core scenarios (build from zero, tidy an existing repo). The agent consults this skill before writing or editing any project code, so the codebase stays coherent by construction. It distills thirteen authoritative references into **four fused usage clusters**, plus a coordination layer, into one shared convention set. Each cluster is one coherent concern the agent reaches for 鈥?the sources inside it compound, they do not stack: 1. **Project Scaffold & Python Grammar** (`references/scaffold_grammar.md`) 鈥?fuses Lightning-Hydra-Template (structure, `LHT-*`), Hydra configs (`HY-*`), and Google Python Style...